K-Mean Algorithm
A Review of K-mean Algorithm
... mining. K-mean is the most popular partitional clustering ...standard k-mean algorithm and analyze the shortcoming of k- mean ...modified k-mean ...
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NORMALIZATION INDEXING BASED ENHANCED GROUPING K-MEAN ALGORITHM
... Simple k mean clustering algorithm has been improved by using ...improved algorithm is named as modified k mean ...This algorithm has been implemented by using C#.NET. In ...
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Image segmentation method based on K-mean algorithm
... The K-mean algorithm has less space requirements be- cause it only needs to store data points and ...+ K), where n is the number of data points and the K-mean algorithm ...
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Implementation of Improved K Mean Algorithm for Intrusion Detection System to Improve the Detection Rate
... to K-Mean algorithm is that it requires the number of clusters as an input to the ...The algorithm is incapable of determining the appropriate number of clusters and depends upon the user to ...
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REMOVAL OF HIGH DENSITY IMPULSE NOISE USING MODIFIED K-MEAN ALGORITHM FOR DIGITAL IMAGE
... modified K-Means algorithm based on the enhancement of the sensitivity of initial center of ...This algorithm divides the whole space into different segments and calculates the frequency of data ...
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Improved K Mean Algorithm for Detection Rate in Intrusion Detection System with AI
... point K-implies grouping calculation was connected into ksubsets on preparing information where k is the quantity of bunches that are required for ...of K-group neuro-fluffy (FNN) was given as ...
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Research on face feature extraction based on K-mean algorithm
... of K-mean to cluster features, because the features in a certain range can be grouped into one class, the robustness of features to interference is ...after K-mean ...
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COMPARISON OF K-MEAN ALGORITHM & APRIORI ALGORITHM – AN ANALYSIS
... Apriori algorithm is an efficient algorithm for finding all frequent item ...Apriori algorithm implements level-wise search using frequent item ...Apriori algorithm can be additionally ...be ...
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Parallel Implementation of Fuzzy Clustering Algorithm Based on MapReduce Computing Model of Hadoop –A Detailed Survey
... The K-mean algorithm faces a problem of giving a hard partitioning of the data which means that each point is dedicated to one and only one ...Fuzzy K-mean clustering [1] (also known as ...
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Study and Implementing K mean Clustering Algorithm on English Text and Techniques to Find the Optimal Value of K
... in k-means algorithm. The correct choice of k is often ambiguous; to solve this problem different practitioner used different approaches Elbow method is also one of them to find the right number of ...
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An Efficient Modified K-Means Algorithm To Cluster Large Data-set In Data Mining
... algorithms K- Means, and proposed algorithm Modified K-Mean were examined and analyzed based on their basic approach for large data set, using student class ...best algorithm in each ...
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K-mean Clustering for Data Mining: A Review
... modified k-mean clustering algorithm to cluster large datasets, the main motive is to find out the cluster centers which are very close to the final result for each iterative ...Modified ...
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Two Layer k means based Consensus Clustering for Rural Health Information System
... As Shown in figure 3, the heterogeneous data is taken as a input to system. This data is collected through Asha's daily collection of information from different regions. After that Consensus clustering is applied on this ...
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Text Clustering Algorithms: A Review
... condition. k-mean algorithm have advantages as the following : simple and uses small number of iteration even less than 5 iteration is sufficient for large data set; can run parallel ;good effect on ...
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A Survey on Automated System for Brain Tumor Detection and Segmentation
... Implanting the K-mean algorithm which consists of multiple phases. First phaseconsists of registration of multiple MR images of the brain taken along adjacent layers of brain. In the second phase, ...
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Classification Connection of Twitter Data using K Means Clustering
... The tweets retrieved would be unstructured initially due to all the metadata associated with it making it ineligible. It is therefore required to identify important words while removing unnecessary text from the tweet ...
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Title: ARTIFICIAL INTELLIGENCE BASED CLUSTER OPTIMIZATION FOR TEXT DATA MINING
... Abstract— The relationship between data mining and evaluation system disciplines need to be emerged and focused for better decision making. Studying the current environment to apply data mining to search, sort, group and ...
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A Comparative Analysis Of Clustering Based Brain Tumor Segmentation Techniques
... Clustering is widely used for segmenting medical images. Clustering algorithms are used to partition a dataset into a certain number of group, subsets or categories, where the data members of each group are similar while ...
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Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition
... ...(9) Here n denotes the number of training samples, and the learning rate. The attentive reader will notice that the unconstrained use of this learning algorithm would drive to infinity because the weight would ...
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An Enhanced Image Retrieval Using K-Mean Clustering Algorithm in Integrating Text and Visual Features
... proposed algorithm integrate textual and visual feature represented text terms in the visual feature space, and developed a text-guided weighting scheme for visual ...
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